RCT may not be feasible all the time. But does it mean that we are just stuck in the first step of Pearl’s causal ladder? This blog introduces two fundamental methods Backdoor and Front-door Adjustment that can be used to answer interventional and counterfactual queries (the 2nd and 3rd steps in Pearl’s causal ladder) if the causal relationship satisfies certain criteria.
Tag Archives: Traditional Machine Learning
Learning Record: Causal Inference [1]
Correlation, or so-called associational relationship, absolutely should never imply the causation, while it is quite common for even some professional statistists to make this mistake. In fact, the debate between correlation and causation has persisted decades: A part of classical statistists, such as Francis Galton and Karl Pearson, insisted that causation is an “anti-scientific” subject. As a result, related exploration was stalled for many years, and some exciting and gratifying advancements are observed still very recent years.
Hand Gesture Recognition for Sign Language
My undergraduate Final Year Project awarded as the Excellent Bachelor’s Project. It develops a vision-based sign language recognition system with multiple machine-learning models, which currently can recognize 10 static and 2 dynamic gesutures in ASL with testing accuracy of 99.68%. Project Abstract Majority of deaf-and-mute people use sign language produced by body actions such asContinue reading “Hand Gesture Recognition for Sign Language”
J. Mao's Tech House 